www.hydrol-earth-syst-sci.net/16/1635/2012/ doi:10.5194/hess-16-1635-2012
© Author(s) 2012. CC Attribution 3.0 License.
Earth System
Sciences
Space-based passive microwave soil moisture retrievals and the
correction for a dynamic open water fraction
B. T. Gouweleeuw1, A. I. J. M. van Dijk1, J. P. Guerschman1, P. Dyce1, and M. Owe2 1CSIRO Land and Water, Canberra, ACT, Australia
2NASA/Goddard Space Flight Center, Greenbelt, MD, USA
Correspondence to: B. T. Gouweleeuw (ben.gouweleeuw@csiro.au)
Received: 31 December 2011 – Published in Hydrol. Earth Syst. Sci. Discuss.: 18 January 2012 Revised: 12 May 2012 – Accepted: 21 May 2012 – Published: 8 June 2012
Abstract. The large observation footprint of low-frequency satellite microwave emissions complicates the interpretation of near-surface soil moisture retrievals. While the effect of sub-footprint lateral heterogeneity is relatively limited under unsaturated conditions, open water bodies (if not accounted for) cause a strong positive bias in the satellite-derived soil moisture retrieval. This bias is generally assumed static and associated with large, continental lakes and coastal areas. Temporal changes in the extent of smaller water bodies as small as a few percent of the sensor footprint size, however, can cause significant and dynamic biases. We analysed the influence of such small open water bodies on near-surface soil moisture products derived from actual (non-synthetic) data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for three areas in Oklahoma, USA. Differences between on-ground observa-tions, model estimates and AMSR-E retrievals were related to dynamic estimates of open water fraction, one retrieved from a global daily record based on higher frequency AMSR-E data, a second derived from the Moderate Resolution Imag-ing Spectroradiometer (MODIS) and a third through inver-sion of the radiative transfer model, used to retrieve soil moisture. The comparison demonstrates the presence of rel-atively small areas (<0.05) of open water in or near the sen-sor footprint, possibly in combination with increased, below-critical vegetation density conditions (optical density<0.8), which contribute to seasonally varying biases in excess of 0.2 (m3m−3) soil water content. These errors need to be ad-dressed, either through elimination or accurate characterisa-tion, if the soil moisture retrievals are to be used effectively in a data assimilation scheme.
1 Introduction
function found, conforming to theory, increasing soil mois-ture retrieval error at higher VOD (Parinussa et al., 2011).
Apart from dense vegetation, conditions under which soil moisture cannot be accurately retrieved from passive mi-crowave sensors include precipitating clouds, snow cover, frozen soil and (inland) surface water (e.g. Njoku et al., 2003; Pellarin et al., 2003; Gao et al., 2006; Owe et al., 2008). Typ-ically, quality control masks are provided to screen data af-fected by these conditions. While most of these masks are dynamic and can be derived from ancillary data, the mask for open water is generally static and considers coastal areas and large continental lakes only. Due to the high dielectric constant of water, however, even a small sub-pixel fraction of open water may result in a non-negligible soil moisture overestimation (e.g. Davenport et al., 2008; Loew, 2008).
The LPRM screens data for snow and frozen surface con-ditions to flag those pixels where the model’s LST is esti-mated to be at or below 273 K (Owe et al., 2008). Further, a mask is applied to remove those data affected by dense vege-tation (VOD>0.8), or where convergence between modelled and observed brightness temperature is considered insuffi-cient (>0.25 K) (De Jeu et al., 2008). Additionally, in line with other global-scale coarse soil moisture data sets based on space-based microwave observations, a static mask is used to delineate coastal areas and (large) permanent inland lakes (Njoku et al., 2003; Scipal et al., 2008a). While static water bodies cause a constant positive bias and do not affect tem-poral patterns, temtem-poral changes in open water fraction are not accounted for. Jones et al. (2009) released a global daily record of land surface parameters retrieved from AMSR-E, which as a first includes a dynamic open water fraction, based on 18.7 GHz H- and V-polarized brightness temper-atures.
In this study, the influence of open water on soil moisture retrieval data (Owe et al., 2008; Jones and Kimball, 2010) was investigated by comparison to on-ground station obser-vations and land surface models (LSMs) estimates for three areas in Oklahoma, USA (Fig. 1). Differences between the ground or model estimates and LPRM retrievals were further evaluated against dynamic estimates of open water fraction.
2 Data
2.1 On-ground observations
Soil moisture observations were taken from 11 stations in the Mesonet observational grid in Oklahoma, USA (Brock et al., 1995). The selected stations are all located within three areas of 4×0.25◦lat/long grid cell size (Fig. 1), which are spa-tially representative of the predominant types of land cover in the area. These are both (wooded) grassland and cropland in the East, predominantly cropland in the west , and wooded grassland in the south-central area, as classified by the 1-km global vegetation data set of the University of Maryland
Fig. 1. AMSR-E 0.25 degree gridded absolute soil moisture
(m3m−3)retrievals (AMSR-E VUA abs) from the Land Parameter Retrieval Model (Owe et al., 2008) for 2 April 2003 (upper panel) and 28 July 2003 (lower panel). White areas indicate grid cells out-side the sensor swath (upper panel, northwest corner) or masked-out grid cells of non-convergence (east and southeast). The AMSR-E grids represented by an Oklahoma (inset, next to key) Mesonet ob-servation stations are bold outlined.
2.2 Model estimates
The land information system (LIS), developed at NASA Goddard Space Flight Center, is an interoperable platform capable of integrating the use of LSMs, data management techniques and high performance computing (Kumar et al., 2006). The community Noah land surface model (Ek et al., 2003) and the Community Land Model version 2:0 (CLM2) (Dai et al., 2002; Zeng et al., 2002) are two of the LSMs currently supported by LIS. Both are stand-alone, 1-D models and are freely available: Noah from the National Centers for Environmental Prediction (www.emc. ncep.noaa.gov/NOAH/) and CLM2 from The National Cen-ter for Atmospheric Research (www.cgd.ucar.edu/tss/clm/ distribution/clm2.1/), respectively.
The LSMs simulate a range of water- and energy balance variables, of which near-surface soil moisture is of inter-est for the present analysis. The models apply finite differ-ence spatial discretisation methods and (semi-)implicit time-integration schemes to numerically integrate the governing equations of the physical processes of the soil-vegetation-snow pack medium, including the surface energy balance equation, the Richards equation for soil hydraulics, the diffu-sion equation for soil heat transfer, the energy-mass balance equation for the snow pack, and equations for the conduc-tance of canopy transpiration.
The LSMs can be applied in either coupled or uncou-pled mode. In this study, the models were applied in un-coupled mode, meaning that some (atmospheric forcing) data are used as input, rather than as a dynamic model state themselves. Here, three sets of forcing data were used, i.e. the NCEP Global Data Assimilation System (GDAS), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the North American Data Assimilation Sys-tem (NLDAS, based on Eta model data of Mesinger, 2000, and supplemented with observation-based precipitation and radiation data as per Cosgrove et al. (2003). The forcing data are eight in total: large-scale precipitation, convec-tive precipitation, specific humidity, surface pressure, down-ward shortwave solar radiation, downdown-ward thermal radia-tion, air temperature, and wind velocity. The temporal and spatial resolution of the NLDAS forcing is 1 h and 0.125◦, while GDAS and ECMWF have a 3 h time step and a 0.25◦ spatial grid (http://ldas.gsfc.nasa.gov/gldas/GLDASforcing. php). The combination of two models (Noah, CLM2) and three forcing data sets created a model ensemble of six mem-bers, of which the spread provides an indication of the range and uncertainty of modelled soil moisture involved. The Noah and CLM models were initialised with a soil mois-ture profile in static gravitational equilibrium and run with one full year (2003) of forcing data. The soil moisture profile at the end of the year was used as the initial soil moisture profile of the next full year run. This was repeated for the number of times it took for the soil moisture profile of con-secutive runs to remain (virtually) unchanged at the end of
the full year run. This soil moisture profile was subsequently used for model initialisation. The model estimates are ob-tained independently of the satellite soil moisture retrievals.
2.3 Satellite observations
Data sets were obtained of near-surface soil moisture re-trievals (∼2 cm) derived from the 6.9 GHz (C-band) and 10.7 GHz (X-band) microwave signal from the AMSR-E in-strument on board the Aqua satellite using the LPRM (Owe et al., 2008). Whilst the LPRM initially provided retrievals of absolute soil moisture (i.e. in the 0–0.5 (m3m−3)range), it currently offers a soil moisture index, which implies re-trievals may be in excess of the 0.5 (m3m−3) threshold. To assess the impact of (re)scaling on the LPRM soil moisture product, both the absolute and relative retrieval are analysed for the year 2003. The AMSR-E footprint is an oval of diam-eter 43×74 km and 30×51 km at C- and X-band, respec-tively, defined as the size of the −3 dB (50 % gain) beam diameter of the radio channel (Gu and England, 2007). By default, the derived soil moisture fields are re-sampled glob-ally to a 0.25◦grid (∼25 km), assigning a value to a grid cell if the centroid of the footprint falls within. It is noted that this extends the nominal footprint area considered to a zone by up to 37 km beyond the grid cell, in case the centroid happens to plot close to the grid cell borderline. Further, as the−3 dB is used as the cut-off gain, the size of the contributing area is in fact larger than the defined footprint. The implication of this is further discussed below.
A subset covering the state of Oklahoma, USA, was sam-pled from the global data set. The choice for this location is motivated by the availability of sets of ground-observed data (Oklahoma Mesonet, Brock et al., 1995) for the period corre-sponding with the satellite data and atmospheric forcing data for the model simulations (i.e. 2003).
All data contaminated by precipitating clouds were re-moved by visual inspection of the LPRM retrieved surface temperature fields and by comparison to ground-observed surface temperature for Oklahoma (Gouweleeuw et al., 2007). Most of the cloud-contaminating conditions occur at daytime in summer, when convection is strong. While this eliminated about 1–3 % of the data set, frozen soil conditions in winter took out the bulk of the data (over 30 %) at this time of year.
3 Methods
Table 1. RMSE (m3m−3)of the current VUA relative soil moisture product vs. ground-observed and modelled soil moisture estimates.
CLM2 Noah
RMSE Grid/Station MESO-NET ECMWF GDAS NLDAS Mean ECMWF GDAS NLDAS Mean
Western area PUTN 0.12 0.056 0.073 0.051 0.056 0.12 0.14 0.11 0.12
WEAT 0.11 0.056 0.075 0.055 0.056 0.12 0.14 0.11 0.12
WATO 0.072 0.071 0.064 0.076 0.065 0.065 0.080 0.058 0.067
HINT 0.064 0.071 0.066 0.079 0.066 0.075 0.094 0.067 0.078
Eastern area HECT-BIXB 0.15 0.17 0.17 0.19 0.18 0.12 0.12 0.12 0.12
PORT 0.19 0.19 0.19 0.22 0.20 0.14 0.13 0.15 0.14
PORT-HASK 0.13 0.19 0.19 0.22 0.20 0.14 0.13 0.15 0.14
HASK 0.093 0.088 0.088 0.107 0.091 0.070 0.074 0.068 0.069
OKMU 0.092 0.084 0.095 0.091 0.088 0.090 0.10 0.082 0.089
South-central area BYAR-VANO 0.14 0.17 0.17 0.19 0.18 0.15 0.13 0.16 0.14
Table 2. RMSE (m3m−3)of the UoM soil moisture product vs. ground-observed and modelled soil moisture estimates.
CLM2 Noah
RMSE Grid/Station MESO-NET ECMWF GDAS NLDAS Mean ECMWF GDAS NLDAS Mean
Western area PUTN 0.19 0.09 0.12 0.081 0.096 0.20 0.22 0.18 0.20
WEAT 0.17 0.083 0.10 0.070 0.084 0.19 0.22 0.18 0.19
WATO 0.19 0.079 0.10 0.068 0.083 0.18 0.20 0.17 0.18
HINT 0.16 0.080 0.10 0.067 0.082 0.19 0.21 0.18 0.19
Eastern area HECT-BIXB 0.14 0.12 0.13 0.10 0.12 0.18 0.18 0.17 0.18
PORT 0.14 0.14 0.14 0.12 0.13 0.20 0.20 0.18 0.20
PORT-HASK 0.21 0.14 0.14 0.12 0.13 0.20 0.20 0.19 0.20
HASK 0.27 0.14 0.14 0.12 0.13 0.20 0.20 0.18 0.20
OKMU 0.18 0.15 0.16 0.14 0.15 0.17 0.19 0.14 0.17
South-central area BYAR-VANO 0.15 0.11 0.12 0.096 0.11 0.13 0.16 0.12 0.13
cloud and soil freezing conditions. Further noise or short-lived events were averaged out by applying a 14-day simple mean low-pass filter to the time series plots (Fig. 2). Next, seasonal effects were identified that could potentially cause temporal bias in the satellite retrievals, viz.: (1) the vegetation cycle; (2) land surface temperature; and (3) open water. This is explored in more detail in the Discussion section. With re-gard to (3), the effect of the open water fraction (OWF) in the passive microwave footprint was assessed in three ways, viz.: (a) using 1 km 16-day composite MODIS reflectance data; (b) from an open water fraction estimate, based on 18.7 GHz H- and V-polarized AMSR-E brightness temper-ature (Jones and Kimball, 2010), made available by the Uni-versity of Montana (hereafter referred to as OWFUoM); and (c) the difference between satellite-observed and modelled brightness temperature, inverting LPRM.
The approach to map open water extent from MODIS data computes an Open Water Index (OWI) from the Nadir-BRDF Adjusted Reflectance (NBAR) product (MCD43B4). This MODIS product provides a 1 km 16-day composite of land surface reflectance, corrected for Bidirectional Reflectance Distribution Function (BRDF) and atmospheric affects. The OWI is computed as the difference of the Global Vegetation Moisture Index (GVMI) and the Enhanced Vegetation Index
(EVI), if EVI<0.2. If EVI≥0.2, OWI = 0 (Guerschman et al., 2008). Validation against higher resolution Landsat map-ping suggests this method performs as well as or better than other commonly used methods (Guerschman et al., 2009). The OWFMODISis computed as the number of 1 km pixels of OWI>0 in a grid cell, divided by the total number of pixels. The third method calculates an open water fraction by solving the following equation for OWFLPRM, which appor-tions the overall observed satellite brightness temperature proportionately between the open water and the land surface:
Tb sat =(1−OWFLPRM)·Tb obs/LSM
+OWFLPRM·Tb water (1)
whereTb satis the satellite-observed brightness temperature,
Tb obs/LSMis the brightness temperature calculated from LSM simulated or ground-observed soil moisture andTb wateris the brightness temperature of water.
Fig. 2. AMSR-E derived, ground-observed and simulated soil moisture for 0.25◦grid cells in the Western (PUTN), South-Central (BYAR-VANO) and Eastern Oklahoma area (HECT-BIXB) (low-pass filter 14 days).
4 Results
4.1 Comparison of on-ground, modelled and satellite-derived soil moisture
Figure 1 shows two examples of 0.25◦ gridded maps of AMSR-E-derived absolute soil moisture using LPRM (here-after referred to as Vrije Universiteit Amsterdam, VUA) of Oklahoma at nighttime (descending orbit) for 2 April and 28 July 2003, together with the three areas of 4×0.25◦cell grid size, within which ground-observed data are sampled. The early spring image shows a distinct east-west gradient (2 April), which is persistent throughout the year, albeit less pronounced in summer, as shown for 28 July. Figure 2 il-lustrates the dynamics of this east-west gradient for the year 2003, plotted together with AMSR-E VUA rel, the current relative VUA retrieval product, and AMSR-E retrievals from Jones and Kimball (2010) (hereafter referred to as Univer-sity of Montana, UoM), ground-observed and mean mod-elled soil moisture for the 0.25◦grid cells in the western area (PUTN), south-central area (BYAR-VANO) and eastern area (HECT-BIXB). In the eastern and south-central areas, both AMSR-E VUA abs and AMSR-E VUA rel soil moistures are much higher than all the other estimates, most notably in the first and last three months of the year. Unsurprisingly, the AMSR-E VUA rel retrieval shows a larger range than the AMSR-E VUA abs product for these areas. The seasonal pattern, however, is virtually identical. This seems to indi-cate that the (re)scaling of the VUA product does not signif-icantly affect the observed pattern. The AMSR-E UoM re-trievals show the lowest soil moisture estimated for all areas, with the exception of the start of the year in the south-central area. While the satellite-derived soil moisture data sets make use of the same sensor, the UoM applies a different method to solve the microwave radiative transfer function for land sur-face variables, using a combination of multi-frequency po-larizations and ratios (Jones and Kimball, 2010). Addition-ally, it employs an open water fraction derived from higher
frequency AMSR-E data to correct for positive bias. Some seasonality is also present in the UoM retrievals in the south-central and eastern area, albeit less pronounced and at a lower level than in the VUA retrievals. Tables 1 and 2 indicate RMSE for the UoM retrievals vs. ground-observed and mod-elled soil moisture is consistently higher than the RMSE for the VUA product in the western area, while it is alternately higher and lower in the other two areas. The UoM product compares best to the CLM2 simulations, which reproduce relatively dry time series of soil moisture with a high dy-namic range (Fig. 2). This is explained by the shallow top soil layer in the model adjusted to∼2 cm to match the AMSR-E sampling depth. The Noah model structure stipulates a soil profile with fixed depths of its 10 layers (Ek et al., 2003). The depth of the top soil layer is fixed at 10 cm, prompting higher average moisture content with a lower dynamic range. The Mesonet observations, taken at 5 cm depth, mostly plot close to the Noah simulations, although they deviate slightly more in the eastern area. In the western area, the AMSR-E products plot closest to the CLM2 simulation, in agreement with the comparable sampling depth.
4.2 Independent estimation of OWF using MODIS imagery
Figure 3 shows two RGB false colour maps (band 7, 2, and 1) of Oklahoma depicting MODIS 16-day composite reflectance data (MCD43B4). The 16-day composites in-clude the dates of the daily soil moisture retrieval maps in Fig. 1 (i.e. 22 March–6 April 2003 (upper panel) and 12–28 July 2003). Pixels of OWI>0 are coloured blue, representing mostly dammed lakes/reservoirs and the wider (>1 km) stretches of streams. At a glance, open water extent appears to be larger in March–April, albeit only marginally.
Fig. 3. RGB false colour maps (band 7, 2, and 1) of Oklahoma
of 1 km 16-day composite MODIS reflectance data (MCD43B4) overprinted with the Open Water Index (OWI) map for 22 March– 6 April 2003 (upper panel) and 12–28 July 2003 (lower panel). Also depicted are the three areas in the Western (PUTN), South-Central (BYAR-VANO) and Eastern Oklahoma area (HECT-BIXB). Pixels of OWI>0 are coloured blue.
sizes are substantially larger than 0.25◦, so OWF
MODIS is also computed for larger grid cells centred in the 0.25◦grid cell. Figure 4 shows OWFMODISremains about constant in the western area, although levels vary slightly with grid cell size. The Eastern area shows a marked seasonal change in OWFMODIS, which increases in level and variation with grid cell size up to 1.25◦. OWFUoMfalls somewhere between the 0.75–1.5◦ OWFMODIS and generally reflects a correspond-ing seasonal pattern, absent in the other two areas. Consider-ing OWFUoMis based on the 18.7 GHz signal of 27×16 km footprint, one would expect a better agreement with the smaller 0.25◦OWFMODIS. One should realise, however, that the OWF estimates are based on independent data and unre-lated methods. Despite that, they show an agreement in level and pattern, in that OWF is absent in the western and south-central area and present and dynamic in the east. By contrast, in the south-central area, neither MODIS nor UoM (Fig. 4, middle panel) indicates the presence of an open water frac-tion to account for the observed LPRM soil moisture retrieval overestimation (Fig. 2, middle panel), with the exception of a small increase in OWFUoMat around day 60. This may be ex-plained by the different passive microwave frequencies, and hence, spatial resolutions, involved. The 6.9 and 10.7 GHz soil moisture retrieval occupies the larger footprint, an oval of
diameter 43×74 km and 30×51 km (and beyond), respec-tively, and is prone to signal smearing, when re-sampled to a 0.25 degree grid, in this particular case from the East. This is much less the case for the smaller 18.7 GHz open water fraction footprint of 27×16 km, which falls almost entirely within the 0.25 degree grid.
4.3 Estimation of OWF by inversion of LPRM
Figure 5 shows OWFLPRMcalculated for the grid cells shown in Figs. 2 and 4, in case the AMSR-E VUA products exceed the modelled or ground-observed soil moisture estimate. As a result, it is calculated for the drier CLM2 simulations in the western area only, reflecting an alternating pattern of dry down and wetting up in the shallow, 2-cm model, top soil layer. In the eastern and south-central area, the alternating OWFLPRMpattern is set on top of a more seasonal variation of OWFLPRM. As discussed above, the computed open wa-ter fraction in the south-central area is presumably the re-sult of an artefact caused by signal smearing of the relatively large 6.9 GHz satellite footprint over a 0.25 degree grid. The OWFUoMin the Eastern area corresponds remarkably well to OWFLPRM, especially the fraction calculated for CLM2. In summer, OWFLPRM(Mesonet, Noah) is lower than or out of phase with OWFUoM. This could indicate additional bias due to another source, e.g. dense vegetation. In the Eastern area, however, the computed OWFLPRMindicates that a small frac-tion (<0.05) of open water in the satellite observation foot-print alone may cause a large positive bias in the soil mois-ture product (>0.2 m3m−3).
5 Discussion
The single year analysis presented here argues that the sea-sonally varying extent of water bodies can explain most of the anomaly between ground observations and model esti-mates of near-surface soil moisture on the one hand, and LPRM-based AMSR-E satellite retrievals on the other. Of the three seasonal effects identified as potentially causing bias in the satellite retrievals, viz.: the vegetation cycle, LST and varying open water extent, the latter is considered the most plausible. This is discussed in more detail below.
Fig. 4. Time series of the MODIS-derived Open Water Fraction for the 0.25◦grids cells in the Western, South-Central and Eastern Oklahoma area for increasing grid cell sizes, together with the UoM open water fraction.
Fig. 5. Time series of the Open Water Fraction calculated with the LPRM, together with the OWFUoM, for the 0.25◦grids cells in the
Western, South-Central and Eastern Oklahoma area (low-pass filter 14 days).
together with the VUA (relative and absolute, 1 frequency) and UoM (3 frequencies) optical vegetation indices for the three areas. The NDVI peaks early and, again, late in the year in the western area. This is somewhat faintly reflected in the VUA VOD, which agrees better with the NDVI in the south-central and eastern area, although it lacks the dynamic range. The UoM VOD is relatively flat and comparatively low in the south-central and eastern area. Following Parinussa et al. (2011), the relatively high VUA VOD peak values in all three areas (0.6–0.7) suggest a soil moisture error estimation of 0.1–0.13 (m3m−3) for 6.9 GHz and 0.15–0.20 (m3m−3)
for 10.7 GHz. While these estimated errors account for more than half of the observed differences in soil moisture esti-mates in value, the seasonal dynamic of the VOD time series is in reverse phase, i.e. the highest soil moisture bias coin-cides with the lowest VOD estimates. An increased VOD value, therefore, may add to the positive (or negative) bias in the soil moisture retrieval without fully accounting for it.
Figure 2 shows that the overestimation of satellite-derived soil moisture estimates in the eastern and south-central area occurs in the colder part of the year, i.e. winter/spring and autumn/winter. The LPRM (Owe et al., 2008) provides LST from 37 GHz brightness temperature observations, which is
used to normalise the lower-frequency brightness tempera-ture (6.6 GHz and 10.7 GHz) for emissivity. In summer dur-ing the daytime, the LST may well exceed the temperature of the deeper emitting soil layer in the lower frequencies, es-pecially in dry conditions. If uncorrected, this may result in an underestimation of the emissivity, i.e. an overestimation of the satellite-derived soil moisture estimate. Conversely, in the cold season, it may lead to underestimation of the soil moisture estimate. Hence, a LST bias would have the inverse effect of what is observed.
Fig. 6. The AMSR-E UoM and VUA VOD retrievals (low-pass filter 30 days), together with area-averaged NDVI.
Fig. 7. Time series from 2003 to (mid-) 2010 of the current VUA rel and UoM soil moisture and VOD products, together with the smoothed
UoM OWF (30-day moving median filter), for the Western (top), South-Central (middle) and Eastern (lower) Oklahoma areas.
Figure 7 shows the time series from 2003 to 2010 of UOM, and to mid-2010 of the current VUA soil moisture and VOD products. Although on-ground soil moisture observa-tions and model simulaobserva-tions have not been analysed, the gen-eral patterns observed for 2003 are replicated in the longer time series. The south-central and eastern areas (two lower panels) show a seasonal positive bias in VUA soil moisture, which for the latter area coincides with an increase of open water fraction most of times, a notable exception is the first half of 2006, for which no OWFUoM data were available. From the time series for the Eastern area (lower panel), how-ever, it appears that an OWFUoMincrease is not equally pro-portional to a positive bias of VUA soil moisture over time all
as mentioned earlier, the times series in Fig. 7 show VOD (and hence the associated soil moisture retrieval error) in re-verse phase with the observed positive soil moisture bias.
Pellarin et al. (2003), using L-band passive microwave ob-servations, estimated that more than a third of the Earth’s ter-restrial surface has>0.01 open water, defined in that case as persistent open water. Open water area, however, is by no means static and may vary for shorter or longer periods of time through e.g. flooding, irrigation, and the seasonal recharge and drying of dammed lakes and wetlands.
The soil moisture missions by ESA (SMOS) and NASA (SMAP) that are currently in progress have set a target ac-curacy of ±0.04 (m3m−3). To achieve this, constant and seasonally varying extent of open water within the foot-print will need to be identified and corrected for in the re-trieval. This study provides an indication that higher fre-quency AMSR-E data provide sufficient spatial resolution to correct for open water fraction contribution on a 0.25 degree grid. On higher spatial resolutions (500 m–1 km), standard MODIS reflectance products are suitable for further verifi-cation. Alternatively, it has been shown that high-resolution active radar observations are well suited for open water map-ping (Wagner et al., 2007; Ticehurst et al., 2009).
Taken together, it would seem that strong gains in the qual-ity and error characterisation of passive microwave soil mois-ture retrievals can be made by a combination of assimilating remote sensing observations of open water area and more so-phisticated signal re-sampling approaches (e.g. Gu and Eng-land, 2007).
6 Conclusions
Open water has a strong passive microwave signature and therefore can produce positive biases in top soil moisture content estimated from passive microwave remote sensing. Seasonal variations in the fraction of open water may further complicate the agreement and accuracy of top soil moisture retrievals. The magnitude of this effect was investigated us-ing top soil moisture estimates for 0.25◦ grid cells in Ok-lahoma, derived from on-ground station observations, land surface models, and the AMSR-E passive microwave satel-lite instrument. Differences between the ground or model estimates and remote sensing retrievals were compared to dynamic estimates of open water fraction retrieved from a global daily record based on higher frequency AMSR-E data (Jones and Kimball, 2010), derived from MODIS (Guer-schman et al., 2009) and calculated through LPRM inversion. The comparison indicates that seasonally varying biases in excess of 0.2 (m3m−3)soil water content can be attributed to the presence of relatively small areas (<0.05) of open wa-ter in the (nominal) footprint, possibly in combination with (or enhanced by) higher, but below-critical vegetation density conditions (optical density<0.8). Given the widespread dis-tribution of small water bodies over the Earth’s land surface
and the large satellite footprint, it is plausible that more of-ten than not current soil moisture products have considerable positive bias and systematic noise. It is shown that retrievals can be improved by considering temporal observations of open water area, but the source of the satellite signal needs to be considered when re-sampling into spatial grid resolutions that are smaller than the source area.
Supplementary material related to this article is
available online at: http://www.hydrol-earth-syst-sci.net/ 16/1635/2012/hess-16-1635-2012-supplement.zip.
Acknowledgements. This research is funded under the Water
Information Research and Development Alliance (WIRADA) between CSIRO and the Bureau of Meteorology (BoM), Project 3.1 “Water Resources Assessment and Water Use Accounting”.
Edited by: Y. Liu
References
Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893, doi:10.5194/hess-14-1881-2010, 2010.
Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., and Hahn, S.: Assimilation of surface and root-zone ASCAT soil moisture products into rainfall runoff modelling, IEEE T. Geosci. Remote, PP, 1–14, doi:10.1109/TGRS.2011.2177468, 2011 Brock, F. V., Crawford, K. C., Elliott, R. L., Cuperus, G. W., Stadler,
S. J., Johnson, H. L., and Eilts, M. D.: The Oklahoma Mesonet: A Technical Overview, J. Atmos. Ocean.-Tech., 12, 5–19, 1995. Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarplay, J. D., and Meng, J.: Real-time and retrospective forcing in the North American Land Data Assimilation Sytsem (NLDAS) project, J. Geophys. Res., 108, 8842–8854, 2003.
Crow, W. T.: A novel method for quantifying value in spaceborne soil moisture retrievals, J. Hydrometeorol., 8, 56–67, 2007. Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G., Bosilovich,
M., Denning, S., Dirmeyer, P., Houser, P., Niu, G., Oleson, K., Schlosser, A., and Yang, Z.-L.: The Common Land Model (CLM), B. Am. Meteorol. Soc., 84, 1013–1023, 2002.
Davenport, A. J., Sandells, M. J., and Gurney, R. J.: The effects of scene heterogeneity on soil moisture retrieval from passive microwave data, Adv. Water Resour., 31, 1494–1502, 2008. De Jeu, R. A. M. and Owe, M.: Further validation of a new
method-ology for surface moisture, and vegetation optical depth retrieval, Int. J. Remote Sens., 24, 4559–4578, 2003.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., and Naeimi, V.: Error characterisation of global active and passive microwave soil moisture datasets, Hy-drol. Earth Syst. Sci., 14, 2605–2616, doi:10.5194/hess-14-2605-2010, 2010.
Draper, C. S., Walker, J. P., Steinle, P. J., de Jeu, R. A. M., and Holmes, T. R. H.: An evaluation of AMSR-E Derived Soil Mois-ture over Australia, Remote Sens. Environ., 113, 703–710, 2009. Drusch, M.: Initializing numerical weather prediction models with satellite-derived surface soil moisture, Data assimilation experi-ments with ECMWF’s Integrated Forecast System and the TMI soil moisture data set, J. Geophys. Res.-Atmos., 112, D03102, doi:10.1029/2006JD007478, 2007.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land-surface model advances in the NCEP operational mesoscale mesoscale Eta model, J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296, 2003.
Gao, H., Wood, E. F., Jackson, T. J., Drusch, M., and Bindlish, R.: Using TRMM/TMI to retrieve surface soil moisture over the Southern United States from 1998 to 2002, J. Hydrometeorol., 7, 23–38, doi:10.1175/JHM473.1, 2006.
Gouweleeuw, B. T., Owe, M., and Holmes, T.: AMSR-E sur-face temperature retrievals: the potential for LSM data assim-ilation, in: MODSIM 2007 International Congress on Mod-elling and Simulation. ModMod-elling and Simulation Society of Australia and New Zealand, edited by: Oxley, L. and Ku-lasiri, D., December 2007, 1709–1715, ISBN:978-0-9758400-4-7, available at: http://mssanz.org.au/MODSIM07/papers/27 s28/ AMSR-E s28 Gouweleeuw .pdf, 2007.
Gu, H. and England, A. W.: AMSR-E data resampling with near-circular synthesized footprint shape and noise/resolution tradeoff study, IEEE T. Geosci. Remote, 45, 3193–3203, doi:10.1109/TGRS.2007.895412, 2007.
Guerschman, J. P., Van Dijk, A. I. J. M., McVicar, T., Van Niel, T., Li, L., and Liu, Y.: Water Balance estimates from satellite obser-vations over the Murray-Darling Basin, Report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project, CSIRO: Water for a Healthy Country National Research Flagship, 2008.
Guerschman J. P., Byrne, G., Gonz´alez-Orozco, C., Van Dijk, A. I. J. M., Lymburner, L., Dyce, P., and Ticehurst, C.: Continental es-timation of sub-pixel standing water fractions using the MODIS sensors: method development and potential application for water accounting and assessment in Australia, in: 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, edited by: Anderssen, R. S., Braddock, R. D., and Newham, L. T. H., Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, p. 3718, ISBN:978-0-9758400-7-8, available at: http://www.mssanz.org. au/modsim09/I10/guerschman.pdf, 2009.
Illston, B. G., Basara, J. B., Fischer, D. K., Elliott, R. L., Fiebrich, C., Crawford, K. C., Humes, K., and Hunt, E.: Mesoscale Mon-itoring of Soil Moisture Across a Statewide Network, J. Atmos. Ocean.-Tech., 25, 167–182, 2008.
Jones, L. A. and Kimball, J. S.: Daily Global Land Surface Param-eters Derived from AMSR-E, version 1.2, 2003–2010, Boulder, Colorado USA: National Snow and Ice Data Center, Digital
me-dia, available at: http://nsidc.org/data/nsidc-0451.html (last ac-cess: December 2011), 2010.
Jones, L. A., Kimball, J. S., Podest, E., McDonald, K. C., Chan, S. K., and Njoku, E. G.: A method for deriving land surface moisture, vegetation, and open water fraction from AMSRE, Int. Geosci. Remote Se., 13–17 July, Cape Town, South Africa, 2009. Koster, R. D., Suarez, M. J., Liu, P., Jambor, U., Berg, A. A., Kistler, M., Reichle, R. H., Rodell, M., and Famiglietti, J.: Realistic ini-tialisation of land surface states: Impacts on subseasonal forecast skill, J. Hydrometeorol., 5, 1049–1063, 2004.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F., and Sheffield, J.: Land In-formation System – An Interoperable Framework for High Res-olution Land Surface Modeling, Environ. Modell. Softw., 21, 1402–1415, 2006.
Loew, A.: Impact of surface heterogeneity on surface soil mois-ture retrievals from passive microwave data at the regional scale: The Upper Danube case, Remote Sens. Environ., 112, 231–248, 2008.
Mesinger, F.: Numerical Methods: The Arakawa approach, horizon-tal grid, global, and limited-area modeling. General Circulation Model Development: Past, Present and Future, edited by: Ran-dall, D. A., International Geophysics Series, 70, Academic Press, 373–419, 2000.
Njoku, E. G., Jackson, T., Lakshmi, V., Chan, T., and Nghiem, S. V.: Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41, 215–229, doi:10.1109/TGRS.2002.808243, 2003.
Owe, M., De Jeu, R. A. M., and Walker, J. P.: A methodology for surface soil moisture and vegetation optical depth retrieval us-ing the microwave polarization difference index, IEEE T. Geosci. Remote, 39, 1643–1654, 2001.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical clima-tology of satellite-derived global land surface moisture, J. Geo-phys. Res., 113, F01002, doi:10.1029/2007JF000769, 2008. Parinussa, R., Meesters, A., Liu, Y., Dorigo, W., Wagner, W., De
Jeu, R.: Error estimates for near-real-time satellite soil mois-ture as derived from the Land Parameter Retrieval Model, IEEE Geosci. Remote S., 8, 779–783, 2011.
Pellarin, T., Wigneron, J., Calvet, J., and Waldteufel, P.: Global soil moisture retrieval from a synthetic L-band bright-ness temperature data set, J. Geophys. Res., 108, 4364, doi:10.1029/2002JD003086, 2003.
Reichle, R. H., Koster, R. D., Liu, P., Mahanama, S. P. P., Njoku, E. G., and Owe, M.: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scan-ning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR), J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033, 2007. Scipal, K., Scheffler, C., and Wagner, W.: Soil moisturunoff re-lation at the catchment scale as observed with coarse resolution microwave remote sensing, Hydrol. Earth Syst. Sci., 9, 173–183, doi:10.5194/hess-9-173-2005, 2005.
Scipal, K., Drusch, M., and Wagner, W.: Assimilation of a ERS scat-terometer derived soil moisture index in the ECMWF numerical weather prediction system, Adv. Water Resour., 31, 1101–1112, 2008a.
structure of global soil moisture data sets, Geophys. Res. Lett., 35, L24403, doi:10.1029/2008GL035599, 2008b.
Ticehurst, C. J, Bartsch, A. , Doubkova, M., and Van Dijk, A. I. J. M.: Comparison of ENVISAT ASAR GM, AMSR-E Passive Mi-crowave, and MODIS Optical Remote Sensing for Flood Moni-toring in Australia, Earth Observation and Water Cycle Science, ESA SP-674, Frascati, Italy, 2009.
van Dijk, A. I. J. M. and Renzullo, L. J.: Water resource monitoring systems and the role of satellite observations, Hydrol. Earth Syst. Sci., 15, 39–55, doi:10.5194/hess-15-39-2011, 2011.
Wagner, W. W., Naeimi, V., Scipal, K., De Jeu, R. A. M., and Mar-tinez Fernandez, J.: Soil Moisture from Operational Meteorolog-ical Satellites, Hydrogeol. J., 15, 121–131, doi:10.1007/s10040-006-0104-6, 2007.
Walker, J. P. and Houser, P. R.: A methodology for initializing soil moisture in a global climate Model: Assimilation of near-surface soil moisture observations, J. Geophys. Res., 106, 761– 774, 2001.
